In an application that makes use of a speech recognition result obtained by a speech recognition engine, as far as the user or the application developer is concerned, a function which enables learning about the graphemes output by the speech recognition engine with respect to a particular phonetic sequence is important to the developer or the user of the application making use of the speech recognition engine. In practice, there is a method in which a speech is input to the speech recognition engine by uttering some words or sentences, and it is checked whether or not the expected speech recognition result is output. This confirmatory method represents the simplest confirmatory method for the purpose of checking whether or not the expected graphemes are output with respect to the phonetic sequence that was input.
However, in the confirmatory method in which a person actually performs a speech input by means of vocalization and checks the speech recognition result, if the words to be checked are large in number, then the task of checking requires effort and cost. Moreover, if the correct speech recognition result is not output, it is difficult to pinpoint the cause such as whether the language model is responsible or whether the acoustic model is responsible.
A method is known in which, using a language model that is created based on the statistic identical to the language model used in a speech recognition engine, kana characters are input; kana-kanji conversion is performed; and a result identical to the result of the speech recognition engine is obtained. However, in this method, a decoder capable of kana-kanji conversion needs to be provided separately from the existing decoder of the speech recognition engine. That is, a total of two decoders, namely, a “decoder of the speech recognition engine” and a “decoder for kana-kanji conversion” need to be disposed. As a result, the configuration of the speech recognition result output device becomes complex and the cost increases.